Medical Xenophobia: The Voices of Women Refugees in Durban, Kwazulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Women refugees are mostly affected due to their specific needs for reproductive health services. In their attempt to utilize reproductive health care services, women refugees face medical xenophobia by the health care professionals. Upon their arrival in South Africa, refugee women do not undergo any screening, and this exposes them to health risks making them more prone to all different types of diseases, as many of them are survivors of rape and other acts of sexual violence. OBJECTIVE: The aim of the study was to describe the voices of women refugees regarding reproductive health services in public health institutions in Durban KwaZulu-Natal METHODS: A qualitative, descriptive design was used. Data was collected through face-to-face interviews with eight women refugees living in Durban, KwaZulu-Natal. Thematic content analysis guided the study. RESULTS: Two main themes emerged from the data: negative experiences/challenges, and positive experiences. The negative experiences included medical xenophobia and discrimination, language barrier, unprofessionalism, failure to obtain consent and lack of confidentiality, ill-treatment, financial challenges, internalised fear, religious and cultural domination, the shortage of staff and overcrowding of public hospitals. The positive experiences included positive care and treatment. CONCLUSION: The study concluded that discrimination and medical xenophobia remain a challenge for women refugees seeking reproductive health services in public health institutions in Durban, KwaZulu-Natal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".